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Trade-off background joint learning for unsupervised vehicle re-identification

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Abstract

Existing vehicle re-identification (Re-ID) methods either extract valuable background information to enhance the robustness of the vehicle model or segment background interference information to learn vehicle fine-grained information. However, these methods do not consider the background information as a trade-off attribute to unite valuable background and background interference. This work proposes the trade-off background joint learning method for unsupervised vehicle Re-ID, which consists of two branches, to exploit the ambivalence of background information. In the global branch, a background focus of the pyramid global branch module is proposed to optimize the sample feature space. The designed pyramid background-aware attention extracts background-related features from the global image and constructs a two-fold confidence metric based on background-related and identity-related confidence scores to obtain robust clustering results during the clustering. In the local branch, a background filtering of the local branch module is proposed to alleviate the background interference. First, the background of each local region is segmented and weakened. Then, a background adaptive local label smoothing is designed to reduce noise in every local region. Comprehensive experiments on VeRi-776 and VeRi-Wild are conducted to validate the performance of the proposed balanced background information method. Experimental results show that the proposed method outperforms the state-of-the-art.

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Data availability

The datasets analyzed in the current study are available in the following ways: The VeRi-776 dataset can be obtained from https://github.com/VehicleReId/VeRi; the VeRi-Wild dataset can be obtained from https://github.com/PKU-IMRE/VERI-Wild.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62076117 and 62166026, the Jiangxi Key Laboratory of Smart City under Grant No. 20192BCD40002 and the Jiangxi Provincial Natural Science Foundation under Grant No. 20224BAB212.

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Authors and Affiliations

  1. School of Software, Nanchang University, Nanchang, 330047, China

    Sheng Wang

  2. School of Mathematics and Computer Science, Nanchang University, Nanchang, 330031, China

    Qi Wang, Weidong Min, Qing Han & Di Gai

  3. Medical Big-Data Center, The Second Affiliated Hospital of Nanchang University, Nanchang, 330000, China

    Haowen Luo

  4. Institute of Metaverse, Nanchang University, Nanchang, 330031, China

    Qi Wang, Weidong Min, Qing Han & Di Gai

  5. Jiangxi Key Laboratory of Smart City, Nanchang, 330031, China

    Qi Wang, Weidong Min, Qing Han & Di Gai

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  1. Sheng Wang
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  2. Qi Wang
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  3. Weidong Min
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  4. Qing Han
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  5. Di Gai
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Correspondence to Weidong Min.

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Wang, S., Wang, Q., Min, W. et al. Trade-off background joint learning for unsupervised vehicle re-identification. Vis Comput 39, 3823–3835 (2023). https://doi.org/10.1007/s00371-023-03034-2

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